Method and apparatus for navigational ray tracing

ABSTRACT

A method of navigational ray casting in a computing device includes: obtaining a distance map having a plurality of cells representing respective sub-regions of an environment containing obstacles; wherein each cell defines a minimum obstacle distance indicating a distance from the corresponding sub-region to a nearest one of the obstacles; selecting an origin cell from the plurality of cells, and setting the origin cell as a current cell; selecting a ray cast direction for a ray originating from the origin cell; retrieving the minimum obstacle distance defined by the current cell; selecting a test cell at the minimum obstacle distance from the current cell in the ray cast direction; determining whether the test cell indicates the presence of one of the obstacles; and when the determination is affirmative, determining a total distance between the origin cell and the test cell.

BACKGROUND

Environments in which objects are managed, such as retail facilities, may be complex and fluid. For example, a retail facility may include objects such as products for purchase, a distribution environment may include objects such as parcels or pallets, a manufacturing environment may include objects such as components or assemblies, a healthcare environment may include objects such as medications or medical devices.

A mobile apparatus may be employed to perform tasks within the environment, such as capturing data for use in identifying products that are out of stock, incorrectly located, and the like. To travel within the environment, the mobile apparatus may be configured to perform mapping and localization functions (e.g. to determine a current location of the mobile apparatus within a map of the environment). Such mapping and localization functions may employ ray casting to determine distances between a location of the mobile apparatus and obstacles represented in the map. Ray casting mechanisms may be computationally expensive, however, requiring the mobile apparatus to query numerous portions of the map, to generate a vector representation of the map, or the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed invention, and explain various principles and advantages of those embodiments.

FIG. 1 is a schematic of a mobile automation system.

FIG. 2A depicts a mobile automation apparatus in the system of FIG. 1.

FIG. 2B is a block diagram of certain internal hardware components of the mobile automation apparatus in the system of FIG. 1.

FIG. 2C is a block diagram of certain internal components of the apparatus of FIG. 1.

FIG. 3 is a flowchart of a method for navigational ray casting at the apparatus of FIG. 2A.

FIG. 4A is an overhead view of an operational environment for the apparatus of FIGS. 2A-2C.

FIG. 4B is an occupancy map corresponding to the environment of FIG. 4A.

FIG. 5A is a distance map generated from the occupancy map of FIG. 4B.

FIG. 5B is a detail view of the portion 508 of the distance map of FIG. 5A.

FIGS. 6A-6B and 7A-7B illustrate a performance of the method of FIG. 3, including repeated performances of blocks 320-335, in connection with the distance map of FIG. 5A.

Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.

The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION

Examples disclosed herein are directed to a method of navigational ray casting in a computing device, comprising: obtaining a distance map having a plurality of cells representing respective sub-regions of an environment containing obstacles; wherein each cell defines a minimum obstacle distance indicating a distance from the corresponding sub-region to a nearest one of the obstacles; selecting an origin cell from the plurality of cells, and setting the origin cell as a current cell; selecting a ray cast direction for a ray originating from the origin cell; retrieving the minimum obstacle distance defined by the current cell; selecting a test cell at the minimum obstacle distance from the current cell in the ray cast direction; determining whether the test cell indicates the presence of one of the obstacles; and when the determination is affirmative, determining a total distance between the origin cell and the test cell.

Additional examples disclosed herein are directed to a mobile automation apparatus, comprising: a memory storing a distance map having a plurality of cells representing respective sub-regions of an environment containing obstacles; wherein each cell defines a minimum obstacle distance indicating a distance from the corresponding sub-region to a nearest one of the obstacles; and a navigational controller connected to the memory and configured to: retrieve the distance map from the memory; select an origin cell from the plurality of cells, and setting the origin cell as a current cell; select a ray cast direction for a ray originating from the origin cell; retrieve the minimum obstacle distance defined by the current cell; select a test cell at the minimum obstacle distance from the current cell in the ray cast direction; determine whether the test cell indicates the presence of one of the obstacles; and when the determination is affirmative, determine a total distance between the origin cell and the test cell.

FIG. 1 depicts a mobile automation system 100 in accordance with the teachings of this disclosure. The system 100 is illustrated as being deployed in a retail environment, but in other embodiments can be deployed in a variety of other environments, including warehouses, hospitals, and the like. The system 100 includes a server 101 in communication with at least one mobile automation apparatus 103 (also referred to herein simply as the apparatus 103) and at least one client computing device 105 via communication links 107, illustrated in the present example as including wireless links. In the present example, the links 107 are provided by a wireless local area network (WLAN) deployed within the retail environment by one or more access points (not shown). In other examples, the server 101, the client device 105, or both, are located outside the retail environment, and the links 107 therefore include wide-area networks such as the Internet, mobile networks, and the like. The system 100 also includes a dock 108 for the apparatus 103 in the present example. The dock 108 is in communication with the server 101 via a link 109 that in the present example is a wired link. In other examples, however, the link 109 is a wireless link.

The client computing device 105 is illustrated in FIG. 1 as a mobile computing device, such as a tablet, smart phone or the like. In other examples, the client device 105 is implemented as another type of computing device, such as a desktop computer, a laptop computer, another server, a kiosk, a monitor, and the like. The system 100 can include a plurality of client devices 105 in communication with the server 101 via respective links 107.

The system 100 is deployed, in the illustrated example, in a retail environment including a plurality of shelf modules 110-1, 110-2, 110-3 and so on (collectively referred to as shelves 110, and generically referred to as a shelf 110—this nomenclature is also employed for other elements discussed herein). Each shelf module 110 supports a plurality of products 112. Each shelf module 110 includes a shelf back 116-1, 116-2, 116-3 and a support surface (e.g. support surface 117-3 as illustrated in FIG. 1) extending from the shelf back 116 to a shelf edge 118-1, 118-2, 118-3.

The shelf modules 110 are typically arranged in a plurality of aisles, each of which includes a plurality of modules 110 aligned end-to-end. In such arrangements, the shelf edges 118 face into the aisles, through which customers in the retail environment as well as the apparatus 103 may travel. As will be apparent from FIG. 1, the term “shelf edge” 118 as employed herein, which may also be referred to as the edge of a support surface (e.g., the support surfaces 117) refers to a surface bounded by adjacent surfaces having different angles of inclination. In the example illustrated in FIG. 1, the shelf edge 118-3 is at an angle of about ninety degrees relative to each of the support surface 117-3 and the underside (not shown) of the support surface 117-3. In other examples, the angles between the shelf edge 118-3 and the adjacent surfaces, such as the support surface 117-3, is more or less than ninety degrees.

The apparatus 103 is deployed within the retail environment, and communicates with the server 101 (e.g. via the link 107) to navigate, autonomously or partially autonomously, along a length 119 of at least a portion of the shelves 110. The apparatus 103, autonomously or in conjunction with the server 101, is configured to continuously determine its location within the environment, for example with respect to a map of the environment. The apparatus 103 may also be configured to update the map (e.g. via a simultaneous mapping and localization, or SLAM, process). As will be discussed in greater detail below, the apparatus 103 can be configured to employ a ray casting process for use in the above-mentioned localization and/or mapping functions.

The apparatus 103 is equipped with a plurality of navigation and data capture sensors 104, such as image sensors (e.g. one or more digital cameras) and depth sensors (e.g. one or more Light Detection and Ranging (LIDAR) sensors, one or more depth cameras employing structured light patterns, such as infrared light, or the like). The apparatus 103 can be configured to employ the sensors 104 to both navigate among the shelves 110 (e.g. according to the paths mentioned above) and to capture shelf data during such navigation.

The server 101 includes a special purpose controller, such as a processor 120, specifically designed to control and/or assist the mobile automation apparatus 103 to navigate the environment and to capture data. The processor 120 can be further configured to obtain the captured data via a communications interface 124 for storage in a repository 132 and subsequent processing (e.g. to detect objects such as shelved products in the captured data, and detect status information corresponding to the objects). The server 101 may also be configured to transmit status notifications (e.g. notifications indicating that products are out-of-stock, low stock or misplaced) to the client device 105 responsive to the determination of product status data. The client device 105 includes one or more controllers (e.g. central processing units (CPUs) and/or field-programmable gate arrays (FPGAs) and the like) configured to process (e.g. to display) notifications received from the server 101.

The processor 120 is interconnected with a non-transitory computer readable storage medium, such as the above-mentioned memory 122, having stored thereon computer readable instructions for performing various functionality, including control of the apparatus 103 to capture shelf data, post-processing of the shelf data, and generating and providing certain navigational data to the apparatus 103, such as target locations at which to capture shelf data. The memory 122 includes a combination of volatile (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor 120 and the memory 122 each comprise one or more integrated circuits. In some embodiments, the processor 120 is implemented as one or more central processing units (CPUs) and/or graphics processing units (GPUs).

The server 101 also includes the above-mentioned communications interface 124 interconnected with the processor 120. The communications interface 124 includes suitable hardware (e.g. transmitters, receivers, network interface controllers and the like) allowing the server 101 to communicate with other computing devices—particularly the apparatus 103, the client device 105 and the dock 108—via the links 107 and 109. The links 107 and 109 may be direct links, or links that traverse one or more networks, including both local and wide-area networks. The specific components of the communications interface 124 are selected based on the type of network or other links that the server 101 is required to communicate over. In the present example, as noted earlier, a wireless local-area network is implemented within the retail environment via the deployment of one or more wireless access points. The links 107 therefore include either or both wireless links between the apparatus 103 and the mobile device 105 and the above-mentioned access points, and a wired link (e.g. an Ethernet-based link) between the server 101 and the access point.

The memory 122 stores a plurality of applications, each including a plurality of computer readable instructions executable by the processor 120. The execution of the above-mentioned instructions by the processor 120 configures the server 101 to perform various actions discussed herein. The applications stored in the memory 122 include a control application 128, which may also be implemented as a suite of logically distinct applications. In general, via execution of the application 128 or subcomponents thereof and in conjunction with the other components of the server 101, the processor 120 is configured to implement various functionality related to controlling the apparatus 103 to navigate among the shelves 110 and capture data. The processor 120, as configured via the execution of the control application 128, is also referred to herein as the controller 120. As will now be apparent, some or all of the functionality implemented by the controller 120 described below may also be performed by preconfigured special purpose hardware controllers (e.g. one or more FPGAs and/or Application-Specific Integrated Circuits (ASICs) configured for navigational computations) rather than by execution of the control application 128 by the processor 120.

Turning now to FIGS. 2A and 2B, the mobile automation apparatus 103 is shown in greater detail. The apparatus 103 includes a chassis 201 containing a locomotive mechanism 203 (e.g. one or more electrical motors driving wheels, tracks or the like). The apparatus 103 further includes a sensor mast 205 supported on the chassis 201 and, in the present example, extending upwards (e.g., substantially vertically) from the chassis 201. The mast 205 supports the sensors 104 mentioned earlier. In particular, the sensors 104 include at least one imaging sensor 207, such as a digital camera, as well as at least one depth sensor 209, such as a 3D digital camera. The apparatus 103 also includes additional depth sensors, such as LIDAR sensors 211. In other examples, the apparatus 103 includes additional sensors, such as one or more RFID readers, temperature sensors, and the like.

In the present example, the mast 205 supports seven digital cameras 207-1 through 207-7, and two LIDAR sensors 211-1 and 211-2. The mast 205 also supports a plurality of illumination assemblies 213, configured to illuminate the fields of view of the respective cameras 207. That is, the illumination assembly 213-1 illuminates the field of view of the camera 207-1, and so on. The sensors 207 and 211 are oriented on the mast 205 such that the fields of view of each sensor face a shelf 110 along the length 119 of which the apparatus 103 is travelling. The apparatus 103 is configured to track a location of the apparatus 103 (e.g. a location of the center of the chassis 201) in the common frame of reference 102 previously established in the retail facility, permitting data captured by the mobile automation apparatus 103 to be registered to the common frame of reference. The above-mentioned location of the apparatus 103 within the frame of reference 102, also referred to as localization, is employed in the generation of paths for execution by the apparatus 103.

The mobile automation apparatus 103 includes a special-purpose navigational controller, such as a processor 220, as shown in FIG. 2B, interconnected with a non-transitory computer readable storage medium, such as a memory 222. The memory 222 includes a combination of volatile (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor 220 and the memory 222 each comprise one or more integrated circuits. The memory 222 stores computer readable instructions for execution by the processor 220. In particular, the memory 222 stores a navigation application 228 which, when executed by the processor 220, configures the processor 220 to perform various functions discussed below in greater detail and related to the navigation of the apparatus 103. The application 228 may also be implemented as a suite of distinct applications in other examples.

The processor 220, when so configured by the execution of the application 228, may also be referred to as a navigational controller 220. Those skilled in the art will appreciate that the functionality implemented by the processor 220 via the execution of the application 228 may also be implemented by one or more specially designed hardware and firmware components, such as FPGAs, ASICs and the like in other embodiments.

The memory 222 may also store a repository 232 containing, for example, one or more maps representing the environment in which the apparatus 103 operates, for use during the execution of the application 228. The apparatus 103 may communicate with the server 101, for example to receive instructions to navigate to specified locations and initiate data capture operations, via a communications interface 224 over the link 107 shown in FIG. 1. The communications interface 224 also enables the apparatus 103 to communicate with the server 101 via the dock 108 and the link 109.

In the present example, the apparatus 103 is configured (via the execution of the application 228 by the processor 220) to perform localization and/or mapping functions, for example to determine a location of the apparatus 103 within the environment based on sensor data (e.g. data received from the LIDAR sensors 211). For example, the apparatus 103 may be configured to assess the likelihood that each of a plurality of candidate localizations within the map of the environment is correct. Such an assessment may be performed by comparing actual sensor data (e.g. LIDAR data) with expected sensor data (i.e. which obstacles from the map are expected to be represented in the sensor data if the candidate localization is correct). The expected sensor data noted above may be generated by generating a plurality of rays extending from the candidate localization in the map, to determine the distance of the nearest obstacle along each ray.

As will be apparent in the discussion below, other examples, some or all of the processing performed by the apparatus 103 may be performed by the server 101, and some or all of the processing performed by the server 101 may be performed by the apparatus 103. That is, although in the illustrated example the application 228 resides in the mobile automation apparatus 103, in other embodiments the actions performed by some or all of the components of the apparatus 103 may be performed by the processor 120 of the server 101, either in conjunction with or independently from the processor 220 of the mobile automation apparatus 103. As those of skill in the art will realize, distribution of navigational computations between the server 101 and the mobile automation apparatus 103 may depend upon respective processing speeds of the processors 120 and 220, the quality and bandwidth of the link 107, as well as criticality level of the underlying instruction(s).

Turning now to FIG. 2C, before describing the actions taken by the apparatus 103 to perform navigational ray casting, certain components of the application 228 will be described in greater detail. As will be apparent to those skilled in the art, in other examples the components of the application 228 may be separated into distinct applications, or combined into other sets of components. Some or all of the components illustrated in FIG. 2C may also be implemented as dedicated hardware components, such as one or more ASICs or FPGAs.

The application 228 includes a ray caster 250 configured to generate the above-mentioned rays to determine, for each ray, a distance along the ray from a candidate localization within the map to an obstacle within the map. The outcome of such ray casting (e.g. for a plurality of rays originating at the candidate localization) may be provided to a localizer 254. The localizer 254, in turn, is configured to determine a localization for the apparatus 103, for example among a plurality of candidate localizations, for use in subsequent functions, such as traveling along a path through the environment. The localizer 254 may implement, for example, a Kalman filter and/or other suitable localization algorithms, employing the data provided by the ray caster as an input.

The functionality of the application 228 will now be described in greater detail. In particular, the ray casting mechanism mentioned above will be described as performed by the apparatus 103. Turning to FIG. 3, a method 300 of navigational ray casting is shown. The method 300 will be described in conjunction with its performance by the apparatus 103, with reference to the components illustrated in FIGS. 2B and 2C.

At block 305, the apparatus 103, and particularly the ray caster 250, is configured to obtain a distance map of the environment in which the apparatus 103 is deployed, such as the facility containing the modules 110 shown in FIG. 1. The distance map contains a plurality of cells representing respective sub-regions of the environment. Each cell defines a minimum obstacle distance, indicating a distance from that cell (i.e. from the sub-region of the environment represented by the cell) to the nearest obstacle defined in the map.

The above-mentioned distance map can be obtained at block 305 by retrieving the distance map from the memory 222, for example from the repository 232 where the distance map was previously stored. In other examples, the distance map can be obtained at block 305 by requesting the distance map from the server 101, where the distance map is stored in the repository 132. The initial generation of the distance map, as well as any updating of the distance map (e.g. responsive to reconfiguration of the environment, such as movement of the shelf modules 110, addition of shelf modules 110, or the like) can be performed at either of the server 101 and the apparatus 103. In the present example, the distance map is generated from an occupancy map of the environment, as will be discussed below in connection with FIGS. 4A-4B and 5A-5B.

FIG. 4A illustrates an overhead view of an environment 400, for example including a room defined by a wall 404. As will be apparent to those skilled in the art, the environment 400 may be represented by an occupancy map, also referred to as an occupancy grid. FIG. 4B illustrates an example occupancy map 408 representing the environment 400 as a plurality of cells 412 each corresponding to a sub-region of the environment 400. The size of the sub-region represented by each cell may be selected based on the required capabilities of the system 100 and the desired navigational accuracy of the apparatus 103. For example, each sub-region may have dimensions of about 5 cm squared, although various other sub-region sizes are also contemplated.

Each cell in the occupancy map 408 has one of two values. In the present example, the cells 412 representing sub-regions where the wall 404 is present are illustrated as black, indicating the presence of an obstacle. The remaining cells, representing free space, are illustrated as white. A variety of other formats may be employed for the occupancy map, such as binary numerical values (e.g. zero for obstructed, space and one for free space, or vice versa), and the like.

Turning to FIG. 5A, a distance map 500 generated from the occupancy map is illustrated. The distance map includes a plurality of cells 504, examples of which are shown as a cell 504 a and a cell 504 b, each representing a sub-region of the environment 400. In particular, the cells 504 represent the same sub-regions as the occupancy map 408 (that is, the top-left cell 412 and the cell 504 a, at the top-left of the distance map 500, both represent the same sub-region of the environment 400). In the distance map 500, however, each cell 504 contains a minimum obstacle distance rather than a simple indication of whether the cell itself is obstructed or not. The minimum obstacle distance, as noted earlier, is the distance from the cell 504 containing the minimum obstacle distance to the nearest obstructed cell 504. Thus, in the example shown in FIG. 5A, the cell 504 a contains the distance from the cell 504 a itself to the cell 504 b (the nearest obstructed cell to the cell 504 a). In the illustrated example, distances are represented in grayscale, with black (e.g. a value of zero) indicating a minimum obstacle distance of zero, meaning that the cell itself is obstructed, and lighter colors representing distances greater than zero. The minimum obstacle distances in the cells 504 may be stored as numerical values, such as distances in meters, inches or the like. In other examples, the minimum obstacle distances in the cells 504 may be encoded as grayscale values (e.g. from zero to 255, with steps between grayscale values corresponding to a predefined physical distance). A subset of minimum obstacle distances in the distance map 500, adjacent to the cells representing the wall 404, are illustrated as grayscale values as noted above.

FIG. 5B illustrates another example format for the storage of minimum obstacle distances in the distance map 500 (specifically, in a portion 508 of the distance map 500), in the form of physical distance values (e.g. in centimeters) rather than as grayscale values. The distances defined in each cell in FIG. 5B represent the distance, in centimeters, from the center of the cell to the center of the nearest cell having a minimum obstacle distance of zero (i.e. the nearest obstructed cell).

Returning to FIG. 3, having obtained the distance map at block 305, at block 310 the apparatus 103 is configured to select an origin cell in the distance map, and to set the origin cell as a current cell for use in subsequent steps of the ray casting process, as will be discussed below. The origin cell is a cell of the distance map corresponding to a localization of the apparatus 103, such as a candidate localization to be assessed in localizing the apparatus 103 within the environment. Turning to FIG. 6A, the distance map 500 is illustrated, with contents illustrated only for the cells 504 corresponding to obstructed sub-regions (i.e. with minimum obstacle distances of zero), to enhance visibility of the elements discussed below. As shown in FIG. 6A, an origin cell 504-o has been selected, for example corresponding to a candidate localization for the apparatus 103. The origin cell 504-o is also set as a current cell 504-c (i.e. at block 310, the origin cell and the current cell 504-o and 504-c are one and the same).

Referring again to FIG. 3, at block 315 the apparatus 103 (and in particular the ray caster 250) is configured to select the direction of the next ray to generate, originating from the origin cell 504-o selected at block 310. The apparatus 103 may be configured to generate a predefined number of rays from each origin cell (e.g. at angular increments of two degrees, for a total of 180 rays from the origin cell 504-o; a wide variety of other sets of rays may be predefined for generation, however). Thus, at block 315 the apparatus 103 is configured to select one of the predefined ray cast directions. Returning to FIG. 6A, a selected ray cast direction 600 extending from the origin cell 504-o is illustrated as a dashed line. The selected ray cast direction may be defined as an angle and a location (e.g. in the frame of reference 102) corresponding to the center of the origin cell 504-o.

Referring again to FIG. 3, at block 320 the apparatus 103 is configured to retrieve the minimum obstacle distance from the current cell 504-c (in this case, also the origin cell 504-o as noted above). As noted above, the minimum obstacle distance is defined by the cell itself (e.g. as a value stored in association with the cell). In the present example, the minimum obstacle distance defined by the cell 504-c is 39 cm, indicating that the nearest obstacle (more specifically, the nearest cell 504 having a minimum obstacle distance of zero) is 39 cm away from the cell 504-c.

Returning once again to FIG. 3, at block 325, the ray caster 250 is configured to select a test cell located at the minimum obstacle distance retrieved at block 320 from the current cell 504-c, in the ray casting direction selected at block 315. In other words, the apparatus 103 is not configured to query cells directly adjacent to the current cell 504-c, but rather to jump ahead along the selected ray casting direction 600 by the minimum obstacle distance of the current cell 504-c. Referring to FIG. 6B, a test cell 504-t is selected at the intersection of the ray casting direction 600 and the minimum obstacle distance of 39 cm (illustrated by a dashed circle with a radius of 39 cm centered at the origin cell 504-o). More specifically, in the present example the minimum obstacle distances correspond to the distances between the centers of cells 504. Therefore, at block 325 the test cell selected is a cell whose center is at or below the minimum obstacle distance, and through which the ray casting direction 600 passes.

Referring again to FIG. 3, at block 330 the ray caster 250 is configured to determine whether the test cell 504-t from block 325 indicates the presence of an obstacle. Specifically, the determination at block 330 is a determination of whether the minimum obstacle distance of the test cell 504-t is zero. If the determination at block 330 is negative (i.e. when the minimum obstacle distance of the test cell 504-t is not zero, indicating that the test cell 504-t represents an unobstructed sub-region of the environment 400), the performance of the method 300 proceeds to block 335.

At block 335, the ray caster 250 is configured to set the test cell 504-t as the current cell 504-c, and to then return to block 320. Referring to FIG. 7, the test cell 504-t has been set as the current cell 504-c (numbered 504-c 2 for clarity). The performance of blocks 320 to 330 are then repeated. In the present example, the minimum obstacle distance defined by the current cell 504-c 2 (previously the test cell 504-t) is 11.2 cm (e.g. see FIG. 5B). Thus, at block 325, a further test cell 504-t 2 is selected at a distance of 11.2 cm along the ray casting direction 600 from the current cell 504-c 2. The further test cell 504 t-2, as shown in FIG. 7A. A further determination at block 330 is negative (the minimum obstacle distance of the test cell 504-t 2 is 15.8 cm), and the performance of block 335 and blocks 320-330 is therefore repeated again, with the test cell 504-t 2 being set as the current cell, and a new test cell being selected.

FIG. 7B illustrates the performance of several further performances of blocks 335 and 320-330, advancing the current cell 504-c through iterations 504-c 3, 504-c 4 504-c 10. At a further performance of block 325, a further test cell 504-t 10 is selected. As is evident from FIG. 7B, the minimum obstacle distance defined by the test cell 504-t 10 is zero, and the determination at block 330 is therefore affirmative. Returning to FIG. 3, the ray caster 250 therefore proceeds to block 340. At block 340, the ray caster determines a total distance between the final test cell 504-t 10 and the origin cell 504-o, for return to another function executed by the apparatus 103, such as the above-mentioned localization filter. The total distance can be determined at block 340 accordingly various suitable mechanisms, as the dimensions of the cells 504 are known, and the positions of the origin cell 504-o and the final test cell 504-t 10 are also known. In the example shown in FIG. 7B, the total distance is 162 cm. Thus, as will now be apparent, the distance from the origin cell 504-o to the first obstacle along the ray cast direction 600 is obtained by querying eleven cells, including the origin cell 504-o, even though the ray cast direction 600 traverses a significantly greater number of cells 504 (about 45 cells) before reaching the final test cell 504-t 10.

Referring again to FIG. 3, at block 345 the ray caster 250 is configured to determine whether any ray casting directions remain to be assessed for the current origin cell 504-o. When the determination is affirmative, the above process is repeated for another ray cast direction, first resetting the origin cell as described in connection with block 310, and then selecting the next ray cast direction at block 315. When the determination is negative, the method 300 ends.

In further embodiments, such as those in which the apparatus 103 is deployed in a dynamic environment (containing moving obstacles), and/or in which the apparatus 103 is deployed to perform a SLAM process in which a map of the environment (and therefore the distance map 305) is generated substantially in real-time. In such embodiments, the performance of block 305 can be repeated to obtain an updated distance map, for example responsive to movement of an obstacle, detection of a new obstacle, or the like.

In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of any or all the claims. The invention is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued.

Moreover in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially”, “essentially”, “approximately”, “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one or more specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein. Alternatively, some or all functions could be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter. 

The invention claimed is:
 1. A method of navigational ray casting in a computing device, comprising: obtaining a distance map having a plurality of cells representing respective sub-regions of an environment containing obstacles; wherein each cell defines a minimum obstacle distance indicating a distance from the corresponding sub-region to a nearest one of the obstacles; selecting an origin cell from the plurality of cells, and setting the origin cell as a current cell, the origin cell corresponding to a candidate localization of a mobile automation apparatus deployed in the environment; selecting a ray cast direction for a ray originating from the origin cell; retrieving the minimum obstacle distance defined by the current cell; selecting a test cell at the minimum obstacle distance from the current cell in the ray cast direction; determining whether the test cell indicates the presence of one of the obstacles; when the determination is affirmative, determining a total distance between the origin cell and the test cell; capturing, via a sensor of the mobile automation apparatus, sensor data indicating an actual distance; and providing the actual distance and the total distance to a localization filter to determine a further candidate localization.
 2. The method of claim 1, further comprising: when the determination is negative, setting the test cell as the current cell; and repeating retrieving the minimum obstacle distance, selecting a test cell, and determining whether the test cell indicates the presence of one of the obstacles.
 3. The method of claim 1, wherein each of the cells defines the minimum obstacle distance as a physical distance value.
 4. The method of claim 1, wherein each of the cells defines the minimum obstacle distance as an intensity value.
 5. The method of claim 1, further comprising: responsive to determining the total distance, determining whether further ray cast directions remain to be processed; and when further ray casting directions remain to be processed, repeating the selecting a ray cast direction, retrieving the minimum obstacle distance, selecting the test cell, and determining whether the test cell indicates the presence of one of the obstacles.
 6. The method of claim 1, further comprising providing the total distance as an input to a localization filter.
 7. The method of claim 1, wherein obtaining the distance map includes retrieving the distance map from a memory of the computing device.
 8. The method of claim 1, wherein obtaining the distance map includes requesting the distance map from a server.
 9. The method of claim 1, wherein obtaining the distance map includes: retrieving an occupancy map having a plurality of occupancy map cells representing the sub-regions; wherein each occupancy map cell defines whether the corresponding sub-region is obstructed or free; and generating the distance map from the occupancy map.
 10. A mobile automation apparatus, comprising: a sensor; a memory storing a distance map having a plurality of cells representing respective sub-regions of an environment containing obstacles; wherein each cell defines a minimum obstacle distance indicating a distance from the corresponding sub-region to a nearest one of the obstacles; and a navigational controller connected to the memory and configured to: retrieve the distance map from the memory; select an origin cell from the plurality of cells, and setting the origin cell as a current cell, the origin cell corresponding to a candidate localization of the mobile automation apparatus in the environment; select a ray cast direction for a ray originating from the origin cell; retrieve the minimum obstacle distance defined by the current cell; select a test cell at the minimum obstacle distance from the current cell in the ray cast direction; determine whether the test cell indicates the presence of one of the obstacles; when the determination is affirmative, determine a total distance between the origin cell and the test cell; capture, via the sensor, sensor data indicating an actual distance; and provide the actual distance and the total distance to a localization filter to determine a further candidate localization.
 11. The mobile automation apparatus of claim 10, wherein the navigational controller is further configured to: when the determination is negative, set the test cell as the current cell; and repeat retrieval of the minimum obstacle distance, selection of a test cell, and determination of whether the test cell indicates the presence of one of the obstacles.
 12. The mobile automation apparatus of claim 10, wherein each of the cells defines the minimum obstacle distance as a physical distance value.
 13. The mobile automation apparatus of claim 10, wherein each of the cells defines the minimum obstacle distance as an intensity value.
 14. The mobile automation apparatus of claim 10, wherein the navigational controller is further configured to: responsive to a determination of the total distance, determine whether further ray cast directions remain to be processed; and when further ray casting directions remain to be processed, repeat the selection of a ray cast direction, retrieval of the minimum obstacle distance, selection of the test cell, and determination of whether the test cell indicates the presence of one of the obstacles.
 15. The mobile automation apparatus of claim 10, wherein the navigational controller is further configured to provide the total distance as an input to a localization filter.
 16. The mobile automation apparatus of claim 10, wherein the navigational controller is further configured, prior to storing the distance map in the memory, to generate the distance map.
 17. The mobile automation apparatus of claim 16, wherein the navigational controller is configured, to generate the distance map, to: retrieving an occupancy map having a plurality of occupancy map cells representing the sub-regions; wherein each occupancy map cell defines whether the corresponding sub-region is obstructed or free; and generating the distance map from the occupancy map.
 18. The mobile automation apparatus of claim 10, further comprising a communication interface; wherein the navigational controller is further configured, prior to storing the distance map in the memory, to request the distance map from a server via the communication interface. 